In order to appreciate new, useful, and innovative aspects of the Needs-Matching Navigator System (hereinafter “MNS”), a closer look at some basic metrics is appropriate; metrics such as “best”, “life-beneficial”, and even “orientation”. These and other terms (as will be described hereinafter) will be used to portray aspects which may be helpful for a user to increase his long-term life-flourishing level; which in-turn are metrics which often may convey vastly different quantitative and qualitative valuations. Simply stated, to teach MNS embodiments, many subjective evaluations (and associated terms of the art) will be objectified in their respective contexts.
For example, “search engines” generally operate to answer user queries. Today, these queries are typically expressed as a series of key words, and are typically understood as a collection of synonyms and phrases, which are built from combinations of those words and synonyms.
Search engines generally bias their results to queries according to a predetermined preference orientation. Some search engines bias “answers” according to commercial interests; such as competing advertising campaigns. Other search engines bias “answers” according to an evolving guess about the profile of the questioner (user). User profiles are typically built from the confluence of user query key words in conjunction with any profile disclosure by the user. In actual use, the user profile is then often combined with presumptions about external profile data; which have a high likelihood of being descriptive of the questioner. In contrast, internal search engines (such as those used at call centers) operate against a background of service agreements, customer contracts, supplier specifications, management policies, and associated documentation libraries; which may be constantly updated or randomly out-of-date. Thus in most cases, an answer from a search engine is biased in favor of various search engine owner interests; even if at the expense of the questioner (user) by providing biased less-than-best query results.
Now, even if a search engine were altruistically trying to provide a “best” answer to a user query, this very notion “best” has a multitude of meanings; both subjective and even objective. A simple notion “best” may indeed be straightforward for single variable equations; where a best result is a maximum or a minimum value result. For slightly more complex multi-variable equations (or more typical simultaneous equations sets), finding “best” may first reduce a problem to a smaller number of aggregated variables. Common examples of dual aggregated variables are “risk” and “reward”, “psychological” and “physical”, “cost” and “benefit”, and the like. However objective such a transformation may seem, subjective factors inherently perturb these assignments and their respective quantification. Accordingly, seeking an optimal trade-off between dual aggregated variables, while simplifying the mathematics required, typically introduces subjective biases; thereby putting the quality of the solution as “best” into doubt.
Even if multivariate problems (the abstract user “query”) could be objectively quantified and mathematically optimized, for long term operations, where many decision are made, a best strategy may not even be the sequence of the best decisions. Some well known examples are the famous gambits and sacrifices in chess, the diversions in battle, and the “false flag” attack in politics. Marketing, health care, game theory, and even complex industrial fabrication protocols all involve searching such respectively complex solution spaces; where often, a novel heuristic will find an example of a class of better results than a currently accepted “best practices” teaches.
Exploring the complexity of finding a best solution to a multivariate problem, one step further, all problems suffer from the influence of hidden variables, combined effects of tertiary factors, flaws in the reduction of the problem to an abstract representation (a query), and falsifiability issues; which are inherent to the “culture” of the user (who brings the query), the search engine or expert (who answers the query), and (returning to the underlying mathematics and logic of the question-answer asymmetry) how the temporal resolution of the respective issues are considered. One instant example of this issue will suffice. If the standard medical protocol says that best practice is for a patient with symptom “A” to be treated with procedure “B”, then the attending physician should still (at least) ask if that is as true for a five year old patient as it would be for a ninety five year old patient. Economic example of this style of strategic error are to be found in countless case studies of corporate history; and in the analysis of the decline of nations and of empires.
Simply stated, there is a longstanding need in the art for improvements in the contextual appreciation of “best”; particularly when sorting answers to a query and considering the respective questioner's circumstances. Returning to the very beginning of this section, “life-beneficial” and even “orientation” represent aspects of appropriate knowledge management that are worthy of consideration; and may be objectified as metrics, per se.
While tersely summarized as a longstanding need, there are interesting, relevant, and significant prior art discussions for human centric data systems; even including associated social networking. Some typical examples are: http://cognexus.org/wpf/wickedproblems.pdf “Wicked Problems and Social Complexity” Chapter 1 of Dialogue Mapping: Building Shared Understanding of Wicked Problems, by Jeff Conklin, Ph.D., Wiley, Oct. 2005. This book is about collective intelligence: the creativity and resourcefulness that a group or team can bring to a collaborative problem. http://www.lume.ufrgs.br/bitstream/handle/10183/25515/000753513.pdf? . . . 1 “Memetic Networks: problem-solving with social network models” Ricardo Matsumura De Araújo; 2010. “We frame problem solving as a search for valid solutions in a state space and propose a model—the Memetic Network—that is able to perform search by using the exchange of information, named memes, between actors interacting in a social network. Such model is applied to a variety of scenarios and we show that the presence of a social network greatly improves the system capacity to find good solutions.”http://www.infosys.tuwien.ac.at/research/viecom/papers/ICSOC2013SCUProvisioning.pdf “Provisioning Quality-aware Social Compute Units in the Cloud” Muhammad Z. C. Candra, Hong-Linh Truong, and Schahram Dustdar; Distributed Systems Group, Vienna University of Technology. “To date, on-demand provisioning models of human-based services in the cloud are mainly used to deal with simple human tasks solvable by individual compute units (ICU). In this paper, we propose a framework allowing the provisioning of a group of people as an execution service unit, a so-called Social Compute Unit (SCU), by utilizing clouds of ICUs. Our model allows service consumers to specify quality requirements, which contain constraints and objectives with respect to skills, connectedness, response time, and cost. We propose a solution model for tackling the problem in quality-aware SCUs provisioning and employ some meta-heuristic techniques to solve the problem. A prototype of the framework is implemented, and experiments using data from simulated clouds and consumers are conducted to evaluate the model.”http://eprints.rclis.org/7971/1/isic98+paper.pdf “Evolving Perspectives of Human Information Behavior: Contexts, Situations, Social Networks and Information Horizons” Diane H. Sonnenwald; University of North Carolina at Chapel Hill. “This paper presents an evolving framework of human information behavior. The framework emerges from theories and empirical studies from a variety of research traditions, including information science, communication, sociology and psychology, that inform our understanding of human information behavior. First, fundamental concepts, such as context, situation, and social networks, are discussed. Using these concepts, a series of propositions that strive to elucidate, that is, provide a framework for exploring, human information behavior are proposed. Information human information behavior, including information exploration, seeking, filtering, use, and communication, are included (to varying degrees) in the framework. The framework also incorporates cognitive, social, and system perspectives. A key conception the framework is the notion of an “information horizon.” Within any context and situation is an “information horizon” in which individuals can act. Information horizons, which may consist of a variety of information resources, are determined socially and individually, and may be conceptualized as densely populated solution spaces. In a densely populated solution space, many solutions are assumed, and the information retrieval problem expands from determining the most efficient path to the best solution, to determining how to make possible solutions visible—to an individual(s) and to other information resources.”
Thus, it is fair to say that there are various search engines, heuristics and systems which consider (or could be used to consider) needs matching; particularly if the needs could be expressed and understood, and particularly if the respective questioner's (user's) “life-beneficial” “orientation” could somehow be appropriately convolved to stratify a cloud of possible respective answers. Alternately stated, there is still an need in the art of needs matching for improvements of needs expression, needs understanding, “life-beneficial” “orientation” convolution, answers stratification, and the like. Furthermore, typically of greater importance, having arrived at a perhaps best set of appropriate answers to a question, there is a longstanding need to “best” match at least one of these answers to a real-world opportunity for a respective realization.
Returning to the example of a medical procedure, the first part of a “best” answer is knowing what the “best” appropriate medical procedure would be, and the next part is (or may be) “best” matching that answer to actual real-world availability within the real constraints of cost, time, and the like. Simply stated, in the context of a user's circumstance, “life-beneficial” and “orientation” are not necessarily synergistic; so navigation of needs-matching is likewise a longstanding problem aspect, in search of improvement.
Another longstanding need is expressed from the emerging abundance of Social Network Facilitator Appurtenances; each, in some aspect, attempting to enable a respective advice networks. Wikipedia's description of “Virtual Community” (VC) suggests that: VC (social networks) “all encourage interaction, sometimes focusing around a particular interest or just to communicate. Some virtual communities do both. Community members are allowed to interact over a shared passion through various means: message boards, chat rooms, social networking sites, or virtual worlds”; and then goes on to describe exemplary VCs that are focused on health, civic participation, and communications. Apparently, all of the examples diverge from scalability, as organizational behavior and economics begin to conflict with the generalization of ordinary use.
Two exemplary Social Network Facilitator Appurtenances are respectively from Microsoft and more recently from Google.
“Windows Live Spaces”, originally released in 2004 as “MSN Spaces” and shut down in 2011, was a set of general-purpose tools for users to reach out to others; by publishing their thoughts, photos and interests. Among its many fatal shortcomings, Windows Live Spaces failed to scale up into the sparseness of cyberspace. Simply enabling interactive web-publishing did not answer amorphous needs of human connectivity; nor was it economically competitive with other similar social networking facilitators.
Google+ (pronounced “Google Plus”; previously called “Google Circles”) is considered the Google's fourth social networking appliance (2011-ongoing), following Google's “Orkut” (2004-ongoing; Brazil only), “Google Friend Connect” (2008-2012), and “Google Buzz” (2010-2011). Google+ apparently serves two functions; giving users a mutual social networking appliance and providing Google with a centralized profile for Google Services (YouTube, Gmail, Google Maps, etc.). While Google+ helps Google to build a unified user-tracking monetization profile, there is no indication that this profile is being applied to altruistically advance the user's wellbeing; neither according to any vague human-centric Google wellbeing concept, nor according to any explicit maturing respective user's wellbeing concept.
Furthermore, since these needs remain unanswered, a plethora of narrow-purpose social networking facilitator systems have emerged; such as “Social Network and Location-Based Employment Placement System and Method” [US 20130073474]—which is “directed to an on-line and mobile location-based system blending social, security and communication components to help persons, including youth, find employment and internship opportunities within a community. Utilizing users' social networks, Geo-location, dynamic and real-time information feeds, and proprietary prediction and security technologies, the disclosed system provides a system to create validated personal profiles for job seekers and posters, to browse and search job listings, to communicate about with other users about employment opportunities. The present invention ('474) also assists job posters and organizations to communicate about available projects within their hyper-local area.” The very narrowness of this '474 system (“without prejudice”) testifies to a knowledge engineering failure; to enable open-purpose needs-matching systems.
Accordingly, the longstanding needs for progress in global peer-to-peer communications, according to best practices of Need-Matching issues (spontaneously arising for respective individuals' problems and circumstances), remain in search of progress; particularly scalable progress. More broadly stated, finding “the anyone who can best contribute to an appropriate solution to a problem of any-someone” would be an altruistic milestone event in human history; and progress in that direction is what each of us (in our hearts) would consider evidence of a better world. In the light of universal access to global communications, no cry for help should go unanswered; and no confusion or ambiguity about the nature of that cry nor the peculiarity of that help should stand as a barrier to enabling best-practices answering.